SoroushVahidi/scidocs huggingface dataset:
Review on fraud detection methods in credit card transactions
(2017)

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions are also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction. To detect fraud behavior, bank and credit card companies are using various methods of data mining such as decision tree, rule based mining, neural network, fuzzy clustering approach, hidden markov model or hybrid approach of these methods. Any of these methods is applied to find out normal usage pattern of customers (users) based on their past activities. The objective of this paper is to provide comparative study of different techniques to detect fraud.
Learning partial differential equations via data discovery and sparse optimization.
(2017)

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
Biomimetic walking robot SCORPION: Control and modeling
(2002)

We present the biomimetic control scheme for the walking robot SCORPION. We used a concept of Basic Motion Patterns, which can be combined in a very flexible manner. In addition our modeling and simulation approach is described, which has been done based on the ADAMS(TM) simulator. Especially the motion patterns of real scorpions were analyzed and used for walking patterns and acceleration of the robot.
A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders
(2007)

Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial datasets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate firstinitial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN. The advantage of the approach is the relative ease and flexibility with which these probabilistic models can be developed.
Dynamic Transfer Learning for Named Entity Recognition
(2018)

State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards lowresource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.
Design a single band microstrip patch antenna at 60 GHz millimeter wave for 5G application
(2017)

This proposed paper, a single band microstrip patch antenna for 5G wireless application is presented. This proposed antenna is suitable for the millimeter wave frequency. The single band antenna consist of new H slot and E slot loaded on the radiating patch with the 50 ohms microstrip line feeding used. This single band antenna is simulated on a Rogers RT5880 dielectric substrate have relative permittivity 2.2, loss tangent 0.0009, and height 1.6mm. The antenna is simulated by Electromagnetic simulation, computer software technology Microwave studio. The proposed single band antenna and simulated result on return loss, VSWR, surface current and 3D radiation pattern is presented. The simulated antenna shows the return loss −40.99dB at 60 GHz millimeter wave 5G wireless application presented.
Data Analytics Using Ontologies of Management Theories: Towards Implementing "From Theory to Practice" (Application Paper)
(2016)

We explore how computational ontologies can be impactful vis-à-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliché, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.
The effects of social media on emotions , brand relationship quality , and word of mouth : An empirical study of music festival attendees
(2015)

This article focuses on two under-researched areas of tourism management e the management of music festivals, and the influence of social media on customer relationships. In the new digital era of marketing communications little is known about how social media interactions with tourism brands affect how consumers think and feel about those brands, and consequently how those interactions affect desired marketing outcomes such as word mouth. Based on the literature, a conceptual model was developed and was tested using structural equation modeling. The results show that social media does indeed have a significant influence on emotions and attachments to festival brands, and that social media-based relationships lead to desired outcomes such as positive word of mouth. © 2014 Elsevier Ltd. All rights reserved.
Computational Sociolinguistics: A Survey
(2016)

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of “computational sociolinguistics” that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction, and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions used in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.
Probabilistic Latent Semantic Analysis
(1999)

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, ma­ chine learning from text, and in related ar­ eas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results. in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of ex­ periments.
Rapid Synthesis of Massive Face Sets for Improved Face Recognition
(2017)

Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.
One-Class Convolutional Neural Network
(2019)

We present a novel convolutional neural network (CNN) based approach for one-class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one-class classification. The proposed one-class CNN is evaluated on the UMDAA-02 Face, Abnormality-1001, and FounderType-200 datasets. These datasets are related to a variety of one-class application problems such as user authentication, abnormality detection, and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at: github.com/otkupjnoz/oc-cnn.
Unsupervised Training Set Generation for Automatic Acquisition of Technical Terminology in Patents
(2014)

NLP methods for automatic information access to rich technological knowledge sources like patents are of great value. One important resource for accessing this knowledge is the technical terminology of the patent domain. In this paper, we address the problem of automatic terminology acquisition (ATA), i.e., the problem of automatically identifying all technical terms in a document. We analyze technical terminology in patents and define the concept of technical term based on the analysis. We present a novel method for labeling large amounts of high-quality training data for ATA in an unsupervised fashion. We train two ATA methods on this training data, a term candidate classifier and a conditional random field (CRF), and investigate the utility of different types of features. Finally, we show that our method of automatically generating training data is effective and the two ATA methods successfully generalize, considerably increasing recall while preserving high precision relative to a state-of-the-art baseline.
Habanero-Java library: a Java 8 framework for multicore programming
(2014)

With the advent of the multicore era, it is clear that future growth in application performance will primarily come from increased parallelism. We believe parallelism should be introduced early into the Computer Science curriculum to educate students on the fundamentals of parallel computation. In this paper, we introduce the newly-created Habanero-Java library (HJlib), a pure Java 8 library implementation of the pedagogic parallel programming model [12]. HJlib has been used in teaching a sophomore-level course titled "Fundamentals of Parallel Programming" at Rice University.
 HJlib adds to the Java ecosystem a powerful and portable task parallel programming model that can be used to parallelize both regular and irregular applications. By relying on simple orthogonal parallel constructs with important safety properties, HJlib allows programmers with a basic knowledge of Java to get started with parallel programming concepts by writing or refactoring applications to harness the power of multicore architecture. The HJlib APIs make extensive use of lambda expressions and can run on any Java 8 JVM. HJlib runtime feedback capabilities, such as the abstract execution metrics and the deadlock detector, help the programmer to obtain feedback on theoretical performance as well as the presence of potential bugs in their program.
 Being an implementation of a pedagogic programming model, HJlib is also an attractive tool for both educators and researchers. HJlib is actively being used in multiple research projects at Rice and also by external independent collaborators. These projects include exploitation of homogeneous and heterogeneous multicore parallelism in big data applications written for the Hadoop platform [20, 43].
A lightweight infrastructure for graph analytics
(2013)

Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.
 We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tackled by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.
A Bridging Model for Parallel Computation
(1990)

The success of the von Neumann model of sequential computation is attributable to the fact that it is an efficient bridge between software and hardware: high-level languages can be efficiently compiled on to this model; yet it can be effeciently implemented in hardware. The author argues that an analogous bridge between software and hardware in required for parallel computation if that is to become as widely used. This article introduces the bulk-synchronous parallel (BSP) model as a candidate for this role, and gives results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
Comparison of Two β-Alanine Dosing Protocols on Muscle Carnosine Elevations.
(2017)

OBJECTIVE
β-alanine (BA) is a nonproteogenic amino acid that combines with histidine to form carnosine. The amount taken orally in individual doses, however, is limited due to symptoms of paresthesia that are associated with higher doses. The use of a sustained-release formulation has been reported to reduce the symptoms of paresthesia, suggesting that a greater daily dose may be possible. The purpose of the present study was to determine whether increasing the daily dose of BA can result in a similar increase in muscle carnosine in a reduced time.


METHODS
Eighteen men and twelve women were randomized into either a placebo (PLC), 6-g BA (6G), or 12-g BA (12G) groups. PLC and 6G were supplemented for 4 weeks, while 12G was supplemented for 2 weeks. A resting blood draw and muscle biopsy were obtained prior to (PRE) and following (POST) supplementation. Plasma and muscle metabolites were measured by high-performance liquid chromatography. The loss in peak torque (ΔPT) was calculated from maximal isometric contractions before and after 250 isokinetic kicks at 180°·sec-1 PRE and POST.


RESULTS
Both 12G (p = 0.026) and 6G (p = 0.004) increased muscle carnosine compared to PLC. Plasma histidine was decreased from PRE to POST in 12G compared to PLC (p = 0.002) and 6G (p = 0.001), but no group x time interaction (p = 0.662) was observed for muscle histidine. No differences were observed for any hematological measure (e.g., complete blood counts) or in symptoms of paresthesia among the groups. Although no interaction was noted in ΔPT, a trend (p = 0.073) was observed.


CONCLUSION
Results of this investigation indicate that a BA supplementation protocol of 12 g/d-1, using a sustained-release formulation, can accelerate the increase in carnosine content in skeletal muscle while attenuating paresthesia.
Meta-awareness, perceptual decoupling and the wandering mind
(2011)

Mind wandering (i.e. engaging in cognitions unrelated to the current demands of the external environment) reflects the cyclic activity of two core processes: the capacity to disengage attention from perception (known as perceptual decoupling) and the ability to take explicit note of the current contents of consciousness (known as meta-awareness). Research on perceptual decoupling demonstrates that mental events that arise without any external precedent (known as stimulus independent thoughts) often interfere with the online processing of sensory information. Findings regarding meta-awareness reveal that the mind is only intermittently aware of engaging in mind wandering. These basic aspects of mind wandering are considered with respect to the activity of the default network, the role of executive processes, the contributions of meta-awareness and the functionality of mind wandering.
Yoga as an adjunctive treatment for posttraumatic stress disorder: a randomized controlled trial.
(2014)

BACKGROUND
More than a third of the approximately 10 million women with histories of interpersonal violence in the United States develop posttraumatic stress disorder (PTSD). Currently available treatments for this population have a high rate of incomplete response, in part because problems in affect and impulse regulation are major obstacles to resolving PTSD. This study explored the efficacy of yoga to increase affect tolerance and to decrease PTSD symptomatology.


METHOD
Sixty-four women with chronic, treatment-resistant PTSD were randomly assigned to either trauma-informed yoga or supportive women's health education, each as a weekly 1-hour class for 10 weeks. Assessments were conducted at pretreatment, midtreatment, and posttreatment and included measures of DSM-IV PTSD, affect regulation, and depression. The study ran from 2008 through 2011.


RESULTS
The primary outcome measure was the Clinician-Administered PTSD Scale (CAPS). At the end of the study, 16 of 31 participants (52%) in the yoga group no longer met criteria for PTSD compared to 6 of 29 (21%) in the control group (n = 60, χ²₁ = 6.17, P = .013). Both groups exhibited significant decreases on the CAPS, with the decrease falling in the large effect size range for the yoga group (d = 1.07) and the medium to large effect size decrease for the control group (d = 0.66). Both the yoga (b = -9.21, t = -2.34, P = .02, d = -0.37) and control (b = -22.12, t = -3.39, P = .001, d = -0.54) groups exhibited significant decreases from pretreatment to the midtreatment assessment. However, a significant group × quadratic trend interaction (d = -0.34) showed that the pattern of change in Davidson Trauma Scale significantly differed across groups. The yoga group exhibited a significant medium effect size linear (d = -0.52) trend. In contrast, the control group exhibited only a significant medium effect size quadratic trend (d = 0.46) but did not exhibit a significant linear trend (d = -0.29). Thus, both groups exhibited significant decreases in PTSD symptoms during the first half of treatment, but these improvements were maintained in the yoga group, while the control group relapsed after its initial improvement.


DISCUSSION
Yoga significantly reduced PTSD symptomatology, with effect sizes comparable to well-researched psychotherapeutic and psychopharmacologic approaches. Yoga may improve the functioning of traumatized individuals by helping them to tolerate physical and sensory experiences associated with fear and helplessness and to increase emotional awareness and affect tolerance.


TRIAL REGISTRATION
ClinicalTrials.gov identifier: NCT00839813.
ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection
(2011)

JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. Attackers like JavaScript-based attacks because they can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that is fast enough to be deployed in the browser. Our approach uses Bayesian classification of hierarchical features of the JavaScript abstract syntax tree to identify syntax elements that are highly predictive of malware. Our experimental evaluation shows that ZOZZLE is able to detect JavaScript malware through mostly static code analysis effectively. ZOZZLE has an extremely low false positive rate of 0.0003%, which is less than one in a quarter million. Despite this high accuracy, the ZOZZLE classifier is fast, with a throughput of over one megabyte of JavaScript code per second.